• The Bulletin of The Korean Astronomical Society
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• v.38 no.1
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• pp.28-28
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• 2013

1990년대 중반 처음으로 행성이 발견되기 이전에는 우리의 행성에 대한 지식은 태양계에 존재하는 행성들에 국한되었다. 이후 행성 발견을 위해 여러 방법이 개발되고, 이어진 관측실험을 통해 행성 발견이 가히 폭발적으로 증가함으로써 외계 행성에 대한 연구는 천문학에서 가장 활발한 연구가 이루어지는 분야가 되었다. 한국 천문학계에서도 외계행성에 대한 연구가 지속적으로 행해져 오고 있다. 특히 중력렌즈 방법을 사용한 외계행성 검출 분야에서 많은 연구가 이루어지고 있다. 본 연설에서는 중력렌즈 현상을 이용하여 행성을 발견할 수 있는 원리, 관측실험, 발견된 행성, 과학적 중요성에 대해 발표한다. 그리고 2014년부터 시행할 대규모 관측실험에 대해서도 설명한다.

• Korean Journal of Social Welfare
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• v.53
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• pp.209-230
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• 2003

This is the study to find out the differences between the mandated reporters who reported child abuse and who did not report and to know which factors affect the reporting behavior The researcher sent questioners to 595 doctors, teachers, nurses and social workers in 3 different cities as a sample. 477 people responded to questioner and only 116 people who said that they have found out abused child during the last year were used for analysis of this research. Among 116 people, only 35.4% reported child abuse. There were significant differences between two groups, groups that reported child abuse and groups that did not report, in attitude about Abuse, the experience of child abuse training and knowledge about abuse. However, the result of Logistic regression showed that reporting behavior are significantly affected by occupation, experience of child abuse training and severity of abuse.

• Journal of Korean Elementary Science Education
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• v.23 no.1
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• pp.61-73
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• 2004

The purpose of this study was to suggest a grounded theory on the process of undergraduate students' generating pattern-knowledge about scientific episodes. The pattern-discovery tasks were administered to seven college students majoring in elementary education. The present study found that college students show five types of procedural knowledge represented in the process of pattern-discovery, such as element, elementary variation, relative prior knowledge, predictive-pattern, and final pattern-knowledge. Furthermore, subjects used seven types of thinking ways, such as recognizing objects, recalling knowledges, searching elementary variation, predictive-pattern discovery, confirming a predictive-pattern, combining patterns, and selecting a pattern. In addition, pattern-discovering process involves a systemic process of element, elementary variation, relative prior knowledge, generating and confirming predictive-pattern, and selecting final pattern-knowledge. The processes were shown the abductive and deductive reasoning as well as inductive reasoning. This study also discussed the implications of these findings for teaching and evaluating in science education.

• Journal of The Korean Association For Science Education
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• v.29 no.6
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• pp.611-625
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• 2009

The purpose of this research is to investigate the characteristics of beginning science teachers' subject matter knowledge (SMK) as revealed in their classroom teaching methods. In this research, we explored six beginning teachers' classroom teaching episodes on the 'work and energy' unit. Using open-ended interviews with the teachers and group discussions taking place on a regular basis to analyze and compare the classes of six beginning teachers, we extracted the features of beginning science teachers' SMK. Using grounded theory methods, the characteristics of beginning science teachers' SMK drawn from this research are: (1) beginning teachers' positivistic epistemology on science, (2) claiming the teacher's authority based on rich subject content knowledge, and (3) beginning teachers' science elitism. These epistemological characteristics are realized such PCK as (1) representational errors caused by the teacher's own science misconception, (2) doing harm to students with too much content knowledge, (3) sporadic content presentation lacking a focus, and (4) surplus class hours with lack of effective science teaching explanations. Suggestions for alternative perspectives on science SMK are presented by experienced teachers. In conclusion, science teachers' SMK is necessary, but not sufficient, for effective teaching. Science teachers' SMK does have an effect on science teaching, mediated by other types of teacher knowledge. The beginning teachers need a systematic support to transform their SMK into a viable PCK.

• Proceedings of the Technology Innovation Conference
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• 2009.02a
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• pp.205-230
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• 2009

기술지식으로서의 특허가 과학지식으로서의 과학논문을 많이 인용하면 할수록 과학지식과 기술지식의 상호작용이 많다고 해석할 수 있고, 또한 인용까지의 평균시차가 짧으면 짧을수록 과학지식과 기술지식의 확산속도가 그만큼 빠른 것으로 해석할 수 있다. 과학논문과 특허를 대상으로 과학집약도가 높은 기술분야, 여러 학문분야를 가장 많이 인용하고 있는 기술분야, 그리고 여러 기술분야에서 가장 많이 인용되는 학문분야를 발견할 수 있다. 또한 하나의 특허가 다른 특허에 인용되는 관계를 통해 피인용특허의 기술분야와 인용특허의 산업분야에 대한 연계 매트릭스를 구성함으로써 기술지식이 파급 연계되는 산업분야를 확인할 수 있다. 여기에서는 이러한 과학기술 지식의 연계관계를 규명함으로써 어떤 유형의 지식이 기술혁신에 있어 가장 기초가 되는지, 어떤 기술 분야가 과학적 지식을 가장 역동적으로 활용하는지, 그리고 산업계의 요구와 가장 긴밀한 관계를 맺고 있는 학문분야는 어떤 분야인지를 분석하고자 한다.

• Journal of Internet Computing and Services
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• v.24 no.5
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• pp.51-66
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• 2023

In this paper, we propose a new type of process discovery framework, which is named as control-path-driven process group discovery framework, to be used for process mining and process reengineering in supporting life-cycle management of business process models. In addition, we develop a process mining system based on the proposed framework and perform experimental verification through it. The process execution event logs applied to the experimental effectiveness and verification are specially defined as Process BIG-Logs, and we use it as the input datasets for the proposed discovery framework. As an eventual goal of this paper, we design and implement a control path-driven process group discovery algorithm and framework that is improved from the ρ-algorithm, and we try to verify the functional correctness of the proposed algorithm and framework by using the implemented system with a BIG-Log dataset. Note that all the process mining algorithm, framework, and system developed in this paper are based on the structural information control net process modeling methodology.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10b
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• pp.187-189
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• 2004

최근 정보기술의 가속적인 발전과 인터넷의 급속한 보급으로 인하여 우리는 다양하고 방대한 양의 지역정보를 접하고 이용하고 있다. 그러나 지역개발사업을 추진하는데 있어서 계획수립이 차지하는 중요성이 매우 큼에도 불구하고 지역을 대표하는 객관적이고 유용한 정보를 찾아내어 지역개발계획수립에 활용하는 예는 거의 없었다. 이에 여러 곳에 산재되어있는 지역정보들을 통합하여 관리하고 이러한 대량의 지역 데이터들로부터 지역을 특징지을 수 있는 보다 현실적이고 유용한 정보를 추출하거나 생성하여 지역정보 분석에 활용하는 방법이 필요하게 되었다. 본 논문에서는 지역개발계획을 수립하는데 있어서 방대한 양의 데이터로부터 유용한 정보를 추출하고 발견하는 지식발견(KDD : Knowledge Discovery in Database)(1) 프로세서의 전체과정에 지역개발계획 수립 목적에 맞추어 지역개발이론에 기초한 지역정보 분석과정을 삽입함으로써 보다 합리적이고 현실적인 지역개발계획이 수립되도록 지원할 수 있는 프로세서를 설계한다.

• KSCI Review
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• v.8 no.2
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• pp.67-74
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• 2001

Recent times have seen an explosive growth in the availability of various kinds of data. It has resulted in an unprecedented opportunity to develop automated data-driven techniques of extracting useful knowledge. Data mining. an important step in this process of knowledge discovery consists of methods that discover interesting. non-trivial and useful Patterns hidden in the data In this paper. we surveyed data mining techniques. We find effective data mining techniques in applying real world. and suggest appropriate application area for the each techniques. We conclude the Paper with some research issues.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.05a
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• pp.617-620
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• 2007

최근 건강에 대한 관심이 높아지면서 의료 분야를 지원하는 애플리케이션 개발이 활발히 이루어지고 있다. 심장의 상태를 곡선으로 나타내는 ECG 를 기반으로 심장병의 유무를 발견하는 애플리케이션은 의료 분야 애플리케이션의 좋은 예라고 할 수 있다. ECG 만으로 질환을 판단하는 것은 제약이 있어, 이를 극복하기 위해 MCG 혹은 가상심장과 같은 다른 자원을 활용하는 것은 좋은 방법이다. 이와 같은 통합 시스템을 지원하려면 각 도메인에 대한 지식이 정의되어야 한다. 이에 본 연구에서는 ECG 와 심장계 질환에 대한 지식을 온톨로지를 이용하여 구축하고 ECG 를 통해 질환을 발견할 수 있는 추론 시스템을 제안하고 프로토타입 시스템을 개발한다.

• Journal of the Korea Society of Computer and Information
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• v.17 no.2
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• pp.139-147
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• 2012

In this paper, we discuss the structure of biological knowledge discovery system based on text mining and automatic inference. Given a set of biology documents, the system produces a new hypothesis in an integrated manner. The text mining module of the system first extracts the 'event' information of predefined types from the documents. The inference module then produces a new hypothesis based on the extracted results. Such an integrated system can use information more up-to-date and diverse than other automatic knowledge discovery systems use. However, for the success of such an integrated system, the precision of the text mining module becomes crucial, as any hypothesis based on a single piece of false positive information would highly likely be erroneous. In this paper, we propose a probabilistic filtering method that filters out false positives from the extraction results. Our proposed method shows higher performance over an occurrence-based baseline method.